Showing papers on "Sentiment analysis published in 1970"

Computing Sentiment Polarity of Texts at Document and Aspect Levels

Abstract: This paper presents our experimental work on two aspects of sentiment analysis. First, we evaluate the performance of different machine learning as well as lexicon based methods for sentiment analysis of texts obtained from variety of sources. Our performance evaluation results are on six different datasets of different kinds, including movie reviews, blog posts and twitter feeds. To the best of our knowledge no such work on comprehensive evaluative account involving different techniques on variety of datasets have been reported earlier. The second major work that we report here is about the heuristic based scheme that we devised for aspect-level sentiment profile generation of movies. Our algorithmic formulation parses the user reviews for a movie and generates a sentiment polarity profile of the movie based on opinion expressed on various aspects in the user reviews. The results obtained for the aspect-level computation are also compared with the corresponding results obtained from the document-level approach. In summary, the paper makes two important contributions: (a) it presents a detailed evaluative account of both supervised and unsupervised algorithmic formulations on six datasets of different varieties, and (b) it proposes a new heuristic based aspect-level sentiment computation approach for movie reviews, which results in a more focused and useful sentiment profile for the movies.

Sentiment Analysis of Food Recipe Comments

Abstract: Sentiment analysis of food recipe comments is to identify user comments about the food recipes to the positive or the negative comments. The proposed method is suitable for analysing comments or opinions about food recipes by counting the polarity words on the food domain. The benefit of this research is to help users to choose the preferred recipes from different food recipes on online food communities. To analyse food recipes, the comments of each recipe from members of the community will be collected and classified to neutral, positive or negative comments. All recipes’ comment messages are processed using text analytics and the generated polarity lexicon. Therefore, the user can gain the information to make a smart decision. The evaluation of the comment analysis shows that the accuracy of neutral and positive comment classification is about 90%. In addition, the accuracy of negative comment identification is more than 70%.